Newcastle University | The Alan Turing Institute
A Tale Told in Three Chapters
Art Getis, Waldo Tobler, and Bill Clark
What is spatial inequality?
The observation of uneven attributes or outcomes over space, for sure. But also the processes and structures producing that unevenness, as well as the impacts of those inequalities.
Spatial inequality and the smart city
Thinking about how emerging technologies intersect with the spatial demography of cities to exacerbate, reproduce, and generate inequalities across areas or groups.
How can we support informed and equitable decision-making around sensor placement, especially what criteria ideal networks might satisfy and the inevitable trade-offs involved?
What is the ”best” allocation of n sensors, given a particular goal?
Decision support tools that visualize options and trade-offs
Coverage for older residents (>65)
Coverage for place-of-work population
But should make us think:
how are our data produced?
how can we do better?
Public transportation heat exposure in a warming world
Thinking about the ways in which health, climate change, and transportation intersect
Only 4 out of 11 London underground lines have air conditioning systems
The average summer temperature in London is expected to increase by 2.7 degrees Celsius by the 2050s
The probability of heatwaves could also increase five-fold and they’re expected to occur every other year
By 2070, the mean maximum air temperature in the UK in August is projected to increase by up to 6 °C in summer compared to 2018
How can we estimate current and future heat exposure on the Tube and who (where) is most affected?
Travel flows (origins and destinations)
Who’s travelling? (demographic and health characteristics)
What’s the temperature on board? (estimated from known station-surface differentials)
Synthetic Population Catalyst (SPC)–A synthetic population that simulates individual-level travel behaviour (homeplace and workplace), travel mode, person and socio-economic factors that allow us to explore heat vulnerability at the individual level
Tube operation timetable–For travel times and route estimation, we use the timetable provided by TfL APIs. Provides accurate estimation whether travellers for each OD-pair will take air-conditioned Tube lines.
Clim-recal–Estimates weather and heat wave days in the past and future on a daily basis in 2.2 km*2.2 km cells covering the entire UK. Local variation in the dataset is used to estimate heat exposure more accurately.
The inequality of heat exposure risk is significant in spatial terms
Trickiness of estimation but lots of useful data that can be brought to bear
But also some of this should be being measured directly!
Making satellite imagery data usable, useful, and used in the social sciences and health
Thinking about how many decades1 we’ve been talking about the potential for satellite imagery to be a social science data game-changer.
Lack of useful data products
Skills and capacity
Data products in formats (and locations) social scientists and health folks are used to
Interfaces that serve data that people want to use
Until pretty recently: high-resolution imagery, plus the tools to ingest, extract, and package at scale
We’ve now got the compute, methods, and sensor quality for satellite imagery to be a game-changer for social science and health research and policy making
1. Imagery innovation–research-ready imagery-based data products, building off and developing innovative computing and AI methods that facilitate efficient automated workflows for measures and indicators, as well as custom-defined geographies and time periods.
2. Data for all–data distribution channels that meet researchers and policymakers where they are, with user-friendly interfaces, familiar file formats, linkage and integration with existing data resources
3. Capability and community–building capacity for understanding and working with imagery and imagery-derived data, growing the user-base and providing thought leadership, and heightening awareness and enthusiasm for the value of imagery
(Not the other way around)
(through the lens of yesterday’s conversation)
Sociotechnical Foundations of GeoAI and Spatial Data Science | Franklin | 27 October 2024